Data mining refers to the technical field of computer analysis of large datasets, in order, for example, to detect patterns, trends, or associations, which may be helpful in determining correlation or causation between events and/or in predicting future events. With related computational techniques, it is possible to process volumes of data, detect a variety of relationships, and/or establish degrees of reliability in a manner that exceeds human capabilities.
However, computational resources are not currently sufficient to fully realize the advantages of data mining and related techniques. For example, available computer processors and memories may not be capable of performing the number of calculations required to analyze a given quantity of data within an available computing time. In some cases, such inability may occur when the desired volume of input data is too large to store or process effectively. In many cases, however, it is the resulting volume of output data (e.g., potential solutions to the problem being analyzed) that is too large, particularly when data mining algorithm(s) being used are exponential in nature. In such cases, even a relatively small quantity of input data may result in a potential solution space that is beyond the abilities of modern computers to compute in an effective manner. Moreover, the resulting output/solution(s) may not provide a result that is most interesting or most useful for the data and purpose being considered.
Frequent item-set mining (FIM) refers to a family of data mining algorithms, sometimes referred to as market basket analysis (MBA) or association rules learning. This family of algorithms provides examples of the types of algorithms referenced above, which are generally intended to discover relations or rules between individual items in large datasets in business analysis, marketing, or other applications.
For example, such algorithms, applied to transaction data collected at a supermarket, can identify association relations or rules among products purchased by customers. Based on the association rules, customer behaviors can be analyzed, e.g., to facilitate product promotions and sales. Some typical FIM algorithms include, for example, the APRIORI algorithm, the ECLAT algorithm, the FP-growth algorithm, and the linear time closed item-set miner (LCM) algorithm.